The ability to accurately predict MHC-restricted peptides that can elicit strong T-cell responses is important not only for the development of vaccine strategies but also for advances in understanding T-cell immunodominance. Current predictive approaches based on using large arrays of overlapping synthetic peptides that map the entire amino acid sequence of an antigenic protein, or on computer-driven algorithms that utilize matrix- and anchor-based motifs, have fundamental shortcomings that skew the predictions of T-cell epitopes. This is largely because these approaches focus almost exclusively on the step of peptide binding to the MHC molecules. Recent advances in our understanding of antigen presentation pathways, have shown that the steps of antigen processing and selection also critically influence the peptide repertoire presented to Tcells. Thus, methods for determining MHC-restricted immunodominant epitopes would be more accurate if all of the steps preceding antigen presentation were inclusively integrated together. The goal of this project is to a develop MHC II cell-free system that replicate key stages of the class II antigen presentation pathway for the purpose of accurately predicting immunodominant epitopes in any antigenic proteins. Recently, we have made considerable progress in developing such a system for MHC class II molecules that utilizes five purified protein components of the class II antigen presentation pathway. Notably, this system yielded physiologically relevant immunodominant epitopes restricted to HLA-DR1. In this proposal, we will optimize this MHC II system and extend it to other human HLA-DR molecules and identify immunodominant epitopes of influenza H1N1 virus. The immunogenicity of the identified peptides will be tested in humanized mice and HLA-matched human subjects. Overall, we propose to develop powerful new research tools to more accurately identify physiologically relevant immunodominant epitopes in any antigenic proteins. By knowing the dominant epitopes in any infectious agents, one can effectively immunize against such antigens. Moreover, by knowing the antigenic targets of auto-reactive T-cells, new strategies can be designed to destroy or inactivate these cells. Our approach may also contribute to identify underlying factors of T-cell immunodominance. Thus, the new technology proposed in this project takes a major leap forward in predicting accurately immunodominant peptides. Project